ArticleJournal of neuroengineering and rehabilitation2021
Classification of Parkinson's disease with freezing of gait based on 360° turning analysis using 36 kinematic features.
Article in Journal of neuroengineering and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.Scientific reports · 2026Article
- Multidimensional factors of health-related quality of life in parkinson's disease using ensemble learning and network analysis.Scientific reports · 2025Article
- Association Between Turn Impairments and Cognitive Function in Parkinson Disease.Aging medicine (Milton (N.S.W)) · 2025Article
- Biomechanical analysis of patients with mild Parkinson's disease during indoor cycling training.Journal of neuroengineering and rehabilitation · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 2025Article
- Deep learning-based detection of affected body parts in Parkinson's disease and freezing of gait using time-series imaging.Scientific reports · 2024Article
- Machine learning approach to classifying declines of physical function and muscle strength associated with cognitive function in older women: gait characteristics based on three speeds.Frontiers in public health · 2024Article
- Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease.Frontiers in aging neuroscience · 2024Article
- Association between Severity of Freezing of Gait and Turning Characteristics in People with Parkinson's Disease.International journal of environmental research and public health · 2022Article
- Interplay between gait and neuropsychiatric symptoms in Parkinson's Disease.European journal of translational myology · 2022Article
- Detection and assessment of Parkinson's disease based on gait analysis: A survey.Frontiers in aging neuroscience · 2022Review
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6 authors.
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Abstract
backgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.
methodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.
resultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.
conclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.
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